Significance and context

With the advent of complete genome sequence information comes the challenge of applying
this information to physiological questions. At issue is the relationship between
genotype and phenotype - both for a given organism in different environments, and
for different organisms. Edwards and Palsson apply systems-based analysis to information
derived from the annotated genomic sequence of the bacterium Haemophilus influenzae Rd and the biochemistry of its metabolic reactions to elucidate the metabolic physiology
of H. influenzae Rd, an approach that can be applied to genotype-phenotype relationships in general.
Having constructed an in silico metabolic genotype, they use it to ascertain critical metabolic components, to distinguish
metabolic phenotypes for a given growth variable, and to determine essential, critical
and redundant metabolic genes through the use of in silico gene deletion. This approach should advance our understanding of the underlying design
principles of organisms, including the regulatory logic controlling metabolic pathways.

Key results

From biochemical and genomic information,the H. influenzae Rd metabolic genotype was defined as including 343 metabolites (m) and 488 metabolic
reactions (n), and its characteristics were studied on the basis of the properties
of its stoichiometric matrix (m x n). Flux-balance analysis using this matrix determined
feasible solutions as the intersection of the solution sets satisfying mass-balance
constraints and physicochemical constraints (for example, minimum and maximum flux
values). Key metabolites of the in silicoH. influenzae metabolic genotype were elucidated by examining the degree of connectivity of each
of the metabolites, as determined by the number of metabolic reactions in which each
of the 343 metabolites was used. Metabolites participating in the greatest number
of reactions were deemed to be critical and included ATP, ADP, inorganic phosphate,
pyrophosphate, carbon dioxide, glutamate, NADP, and NADPH, indicating that the generation
of charged cofactors is critical. The capacity of a metabolic genotype to produce
these cofactors was determined by optimizing their production in the linear programming
problem and comparing the charged cofactor capacities of H. influenzae and E. coli genotypes (see Figure 1).

In silico gene deletion experiments examined the effects of alterations in the metabolic genotype
and the number of genes essential for H. influenzae Rd growth in defined media. Edwards and Palsson find that optimal use of the central
metabolic pathways may be fundamentally different according to growth conditions,
and that the number of redundant genes is greatly reduced when the analysis encompasses
different growth conditions.

The figure illustrates the six distinct metabolic phenotypes derived from the H. influenzae Rd in silico metabolic genotype when optimized for maximal biomass production under different
conditions (mixed substrate with varied glutamate and fructose uptake rates). These
metabolic phenotypes employ different combinations of pathways to optimize growth
depending upon substrate availability, each with different constraining features that
reflect the critical components (such as ATP) defined by connectivity analysis. In
addition, they illustrate the complex relationship between pathway utilization and
growth conditions.

Reporter's comments

Use of this methodology requires complete, annotated genomic sequence and extensive
biochemical information for the prokaryote in question. Tools such as KEGG (see above)
are available to aid in defining the stoichiometric matrix. This paper beautifully
couples phenotype to genotype and provides a mathematical framework for this coupling
using flux-balance analysis that should be applicable not only for any prokaryote,
but also for diverse phenotypes (that is, not just maximal growth). In addition, it
shows the utility of in silico biology to elucidate complex relationships and to direct experimental work.

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